Structure identification of Bayesian classifiers based on GMDH

Authors:
Jin Xiao;Changzheng He;Xiaoyi Jiang
Affiliations:
School of Business Administration, Sichuan University, Chengdu, Sichuan 610064, China;School of Business Administration, Sichuan University, Chengdu, Sichuan 610064, China;Department of Computer Science, University of Münster, Einsteinstr. 62, Münster 48149, Germany
Venue:
Knowledge-Based Systems
Year:
2009

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Abstract

This paper introduces group method of data handing (GMDH) theory to Bayesian classification, and proposes GMBC algorithm for structure identification of Bayesian classifiers. The algorithm combines two structure identification ideas of search & scoring and dependence analysis, and is able to accomplish the process of adaptive structure identification. We experimentally test two versions of Bayesian classifiers (GMBC-BDe and GMBC-BIC) over 25 data sets. The results show that, the structure identification of the two Bayesian classifiers especially GMBC-BDe is very effective. And when the data sets contain lots of noise, the superiority of Bayesian classifiers learned by GMBC is more obvious. Finally, giving a classification domain without any prior information about the noise, we recommend adopting GMBC-BDe rather than GMBC-BIC.